Estimating the probability of arbovirus outbreaks in large southern European city infested by Aedes albopictus
Bibliographic record
Abstract
Background The presence of the mosquito species Aedes albopictus, competent vector of Chikungunya (CHIKV) and Dengue (DENV), and the possible arrival of infected travellers returning from endemic countries may represent a public health risk for Southern European countries. The aim of this work was to assess the weekly risk of CHIKV, DENV and ZIKA virus outbreaks in Rome tackling both the risk of infected-host introduction and patterns of local transmission. Methods The probability of infected-host introduction was estimated by a binomial process dependent from the number of infected cases in endemic country and the probability of travelling to Rome. A geometric process with means R0HV (reproductive number for host to mosquito transmission) and R0VH (mosquito to host) estimated the probability of successful transmission. Outbreak probability was estimated in 3 scenarios of different vector-host contact ratio and 2 scenarios of epidemic outbreaks in 5 different endemic countries. Weekly data of global cases and inbound and outbound Rome travellers were joined with field-derived estimates of A. albopictus abundance within the city. Results The model correctly estimated the number of DENV and CHIKV imported cases notified to the national health system. The estimated outbreak probability was ≤1% for both DENV and CHIKV under scenarios of low vector-host contacts, but the risk increased significantly (i.e. DENV outbreak risk =21%; CHIKV outbreak risk=47%; null for Zika) under a scenario of higher vector abundance, still consistent with the abundance data from infested hot spots within the urban area. Conclusion This work disentangles the role of seasonality of mosquito dynamics, traveller’s inflow and temporal pattern of infected cases in endemic countries in building up an outbreak risk model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".